Everything You Need to Know About Object Detection Systems

With the advent of deep learning, implementing an object detection system has become fairly trivial. There are a great many frameworks facilitating the process, and as I showed in a previous post, it's quite easy to create a fast object detection model with YOLOv5. However, understanding the basics of object detection is still quite difficult. It involves a lot of math, and the variable number of outputs/bounding boxes makes it harder to understand than image classification, where we know the number of outputs beforehand. With so many moving parts and new concepts introduced over the history of object detection, it certainly hasn't gotten easier. In this post, I'll distill all this history into a simple guide that explains all the details of object detection and instance segmentation systems. The classic image classification problem is very well known: given an image, can you find the class the image belongs to? We can solve any new image classification problem with ConvNets and transfer learning using pre-trained nets where Convnets are fixed feature extractors.